جست‌وجوی استاد و پژوهشگر

• Parameterized Algorithms
• Graph Theory
• Computational Complexity
• +۴ مورد دیگر

Robert Ganian is an Associate Professor at the Vienna University of Technology (TU Wien) in the Department of Algorithms and Complexity. His research focuses on parameterized complexity, graph algorithms, computational complexity, and their applications in artificial intelligence and graph drawing. He leads projects funded by institutions like the Austrian Science Fund (FWF) and the Vienna Science and Technology Fund (WWTF). Education and Roles: Holds a PhD and has been affiliated with TU Wien since 2018. Teaches courses including Algorithmics, Logical Methods in Computer Science, and Research Seminars. Research Interests: Specializes in parameterized algorithms for graph problems, structural graph theory, and complexity analysis of AI-related tasks. Current projects include 'Parameterized Graph Drawing' and 'Parameterized Analysis in Artificial Intelligence'. Grants and Projects: Active in EU-funded and national projects (e.g., WWTF, FWF). Leads research on algorithmic approaches to graph visualization and computational complexity challenges in AI. Labs/Teams: Part of the Logic and Computation group (LogiCS) and collaborates with international researchers in theoretical computer science and discrete mathematics.

• Machine Learning
• Electronic Commerce
• Information Retrieval
• +۷ مورد دیگر

Wolfdieter Merkl is an Associate Professor at the Department of Data Science, Technische Universität Wien. His research focuses on Machine Learning, Electronic Commerce, and Information Retrieval, with interdisciplinary applications in Art History, E-Learning, and Digital Humanities. He leads projects like the Virtual 3D Social Experience Museum (VSEM), funded by the Austrian Science Fund (FWF). His work integrates computational methods with cultural heritage, including visualizing art historical networks, developing serious games for art education, and analyzing social media interactions. He teaches courses such as E-Marketing and Social Media, emphasizing practical applications of data-driven approaches. Supervising numerous theses, he has guided research on topics like propaganda detection, climate change analysis, and sentiment analysis in finance. Key contributions include data-driven art history mappings, user-generated content analysis in cultural contexts, and 3D virtual world applications. His publications span art history informatics, educational technology, and computational methods in cultural studies.

• Combinatorial Optimization
• Metaheuristics
• Evolutionary Computation
• +۸ مورد دیگر

Günther Raidl is an Associate Professor at the Vienna University of Technology's Faculty of Informatics, specializing in the Algorithms and Complexity department. His research focuses on combinatorial optimization, metaheuristics, evolutionary computation, and operations research, with applications in logistics, bioinformatics, and scheduling. He leads projects on algorithmic problems, including vehicle routing, scheduling, and network design. His academic career includes extensive contributions to heuristic optimization techniques, mathematical programming, and the development of efficient algorithms. He has collaborated with institutions like Honda Research Institute Europe and Alfie's GmbH on projects involving e-mobility optimization, battery station planning, and dynamic vehicle routing. Raidl's work emphasizes practical applications of theoretical research, bridging algorithm development with real-world challenges in logistics and transportation. His recent publications address problems like longest common subsequence algorithms, Roman domination in graphs, and scheduling with interactive human availability learning. Key projects include studies on cooperative optimization for service point distribution, electric dial-a-ride systems, and battery swapping station location planning. He teaches courses on heuristic optimization, algorithms, and mathematical programming, contributing to both education and industry-relevant research.

Hannes Kaufmannمشاهده پروفایل

استاد

• Augmented Reality
• Virtual Reality
• Human-Computer Interaction
• +۷ مورد دیگر

Hannes Kaufmann is a Full Professor and Head of the Research Unit at the Institute of Visual Computing and Human-Centered Technology, TU Wien. His work focuses on Virtual and Augmented Reality (VR/AR), with expertise in tracking technologies, human-computer interaction, and immersive systems. He leads a team developing large-scale VR platforms like ImmersiveDeck and ProjectVerse, enabling collaborative experiences in fields like industrial design and emergency training. Education: PhD in Geometry Education via Augmented Reality (TU Wien), Habilitation in Mixed Reality Applications Research Interests: Mobile AR/VR, Haptics, Wide-Area Tracking, Medical AR, 3D UI Design, Cyberpsychology His research bridges technical innovation with real-world applications, such as disaster simulation, medical training, and accessibility solutions for hearing-impaired users using vibrotactile feedback. He has pioneered systems like the Holodeck-inspired CoboDeck and the AR monitoring framework for industrial quality control (AR-AQ-Bau). Publications span topics from haptic device design (Action-Origami) to collaborative VR training for first responders. Awards include the 2023 SIGGRAPH Best Project Award for haptic innovation and the 2020 nexus health innovation award for SPECTAS eye-tracking technology. Labs/Teams: Mixed Reality Lab, Virtual Meeting Room Initiative Grants: Supported by the Austrian Federal Ministry for Climate Action and EU Industry 4.0 projects

Jiri Hladuvkaمشاهده پروفایل

پژوهشگر

• Computer Vision
• Medical Image Analysis
• Machine Learning
• +۳ مورد دیگر

Projektass. Dr.techn. Jiri Hladuvka is a PostDoc Researcher at Vienna University of Technology, affiliated with the Mixed Reality Lab. His research focuses on computer vision, medical image analysis, and machine learning applications in healthcare. Key areas include image segmentation, graph-based algorithms, and topological data analysis. Education: Holds a Dr.techn. (Doctor of Engineering) degree. Research Interests: His work spans medical imaging analysis using deep learning, validation of image segmentation techniques, and visualization systems for neurobiology. He explores methods like Local Binary Patterns, graph contraction, and outlier detection to improve accuracy in medical diagnostics and image processing. Publications: Recent works include advancing early osteoarthritis detection via texture features, validating OCT scan segmentations, and developing visualization tools for neurobiological data. His contributions highlight interdisciplinary approaches in computer science and biomedical applications. Labs/Teams: Active in the Mixed Reality Lab, focusing on innovative visualization and data analysis solutions.

Alexander Pachaمشاهده پروفایل

مدرس

• Optical Music Recognition
• Deep Learning
• Music Information Retrieval
• +۳ مورد دیگر

Dr. Alexander Pacha is an External Lecturer at TU Wien, specializing in Optical Music Recognition (OMR) and deep learning applications. His work focuses on advancing OMR systems through graph neural networks, dataset development (e.g., DeepScoresV2), and music score analysis. He leads projects like DeepOMR and contributes to the Mixed Reality Lab and Virtual Meeting Room initiatives. Key research areas include music symbol detection, staff/measure segmentation, and cross-document alignment in music scores. Research interests span computer vision, artificial intelligence, and software tools for music information retrieval. His work bridges theoretical advancements with practical applications in digital libraries and music technology. Notable contributions include self-learning OMR frameworks and benchmarking tools for music object detection. Labs & Teams: Mixed Reality Lab (exploring AR/VR in music contexts), Virtual Meeting Room (collaborative digital environments).

• Systems Engineering
• Internet of Things (IoT)
• Cyber-Physical Systems
• +۳ مورد دیگر

Amirali Amiri is a PostDoc Researcher at TU Wien's Department of Automation Systems, part of the Faculty of Informatics. His work focuses on systems engineering, IoT integration, and cyber-physical systems. He teaches courses like Fundamentals of Digital Systems and Computer Engineering Project . His research emphasizes supply chain optimization, IoT performance, and IT/OT convergence security. Notable contributions include model-based approaches for IIoT lifecycle management and self-adaptive systems. Recent studies explore MQTT broker deployment architectures, semantic annotation for runtime models, and graph-based safety analysis. His work bridges theoretical frameworks with empirical validation in industrial automation contexts. Amiri has contributed to conferences such as IEEE ICM, ICPS, and ETFA, addressing challenges in cloud-based routing, cybersecurity, and DevOps integration.

• Algorithms
• Optimization
• Metaheuristics
• +۳ مورد دیگر

Maria Bresich is a PreDoc Researcher at the Faculty of Informatics , Vienna University of Technology , specializing in algorithms and optimization. She works in the Department of Algorithms and Complexity (E192-01) and focuses on hybrid metaheuristics, integer programming, and dynamic routing problems. Her research includes projects on: Dynamic vehicle routing with reinforcement learning Electric Dial-A-Ride optimization with on-the-fly charging station insertion Directed Feedback Vertex Set Problem using hybrid approaches She has supervised diploma theses on elevator control optimization and presented her work at conferences including GECCO and the HRI European Graduate Network Symposium.

Michael Bronsteinمشاهده پروفایل

استاد

• Geometric machine learning
• Deep learning on graphs
• 3D shape analysis
• +۷ مورد دیگر

Michael Bronstein is a Professor & Chair in Machine Learning and Pattern Recognition at the Department of Computing, Imperial College London (since 2018), with a concurrent professorship at the Institute for Computational Science, University of Lugano, Switzerland (since 2010, currently on leave). His academic career includes visiting positions at Stanford University (Visiting Lecturer, 2008-2009), Tel Aviv University (Visiting Associate Professor, 2015-2017), Harvard University (Visiting Scholar, 2017-2018), and MIT (Research Affiliate, 2017-2018). Dr. Bronstein's research focuses on theoretical and computational geometric methods for data analysis, with applications spanning machine learning, computer vision, graphics, geometry processing, biology, and social networks. His work in geometric deep learning has established foundational frameworks for processing non-Euclidean data such as graphs and manifolds, bridging the gap between traditional Euclidean deep learning and complex structured data. His publication record demonstrates a clear trajectory from fundamental geometric methods to their application in diverse domains. Early work focused on 3D shape analysis and non-rigid shape recognition, while more recent publications showcase the application of geometric deep learning to protein structure analysis, point cloud processing, and graph-based machine learning. The consistent thread throughout his research is the development of mathematical frameworks that respect the intrinsic geometry of data. Royal Academy of Engineering Silver Medal (2020) Fellow of IEEE, IAPR, and ELLIS Five ERC grants (Starting 2012, Consolidator 2016, and Proof of Concept 2016, 2018, 2019) Royal Society Wolfson Research Merit Award Young Scientist, World Economic Forum (2014) Bronstein has successfully translated academic research into practical applications through multiple entrepreneurial ventures. As ML Lead for Project CETI and Head of Graph Learning Research at Twitter (since 2019), he bridges academic research with industrial applications. His previous startups include Fabula AI (acquired by Twitter in 2019) and Invision (acquired by Intel in 2012), where he contributed to the development of Intel RealSense 3D camera technology. His research has been supported by significant funding including ERC grants and industry awards from Google, Amazon, and Facebook.

• Complex Systems
• Network Theory
• Financial Networks
• +۲ مورد دیگر

Guido Caldarelli is a Full Professor of Theoretical Physics at Ca' Foscari University of Venice , with affiliations at CNR-ISC, IMT Lucca, and the London Institute for Mathematical Sciences (LIMS). His career spans roles including President of the Complex Systems Society (2018-2020), co-founder of the Network Science Society, and director of CNR-ISC. Active in interdisciplinary research, he bridges statistical physics, network science, and applications in finance, urban planning, and biomedicine. PhD in Condensed Matter Physics, SISSA Trieste (1996) MPhil in Physics, SISSA (1994) Physics Degree, Sapienza Rome (1992) Guido's research focuses on complex systems and network theory , particularly in financial networks, urban dynamics, and biological systems. His work explores scale-free networks, systemic risk, and entropy-based models, with applications in digital twins for cities, microbiome analysis, and misinformation spread on social media. Recent publications highlight trends in network analysis for urban planning (green infrastructure), machine learning in NMR spectroscopy, and social media dynamics during political events. His interdisciplinary approach connects theoretical physics to practical challenges in healthcare, finance, and climate resilience. Scientific Awards Honorary ISI Fellow APS Fellow (2020) Member of Academia Europaea (2020) Fellow of the Network Science Society Outstanding Service Prize, Network Science Society (2020) Guido leads the Complex Networks group at CNR-ISC, co-founded Catchy s.r.l. (data-driven innovation), and serves on the Scientific Committee for Italian Police Force training . His work integrates theoretical rigor with real-world impact across disciplines.

Leslie Ann Goldbergمشاهده پروفایل

استاد

• Algorithms
• Complexity Theory
• Randomized Algorithms
• +۶ مورد دیگر

Leslie Ann Goldberg is Professor of Computer Science at the University of Oxford and Senior Research Fellow at St Edmund Hall. She has held academic positions at the University of Liverpool (2006-2013) and the University of Warwick (1995-2006), and previously worked at Sandia National Labs (1992-1995). Currently serving as Head of Department (sabbatical planned for 2025-26), she leads research in theoretical computer science with focus on algorithms and complexity theory. Her research interests span algorithms for approximately counting and randomly sampling combinatorial structures , analysis of Markov-chain based sampling algorithms , complexity of counting , and stochastic processes in computer science . She specializes in randomized algorithms, particularly their mathematical analysis regarding runtime and accuracy. Current projects include contentions resolution via backoff protocols, random sampling and approximate counting algorithms, counting complexity theory, and analyzing processes on networks like the Moran process. Professor Goldberg has published over 100 papers in top theoretical computer science venues, with recent work focusing on spin systems, graph homomorphisms, subgraph counting, and network processes. Her publications show consistent output in premier venues including ICALP (where she has won three Best Paper Prizes), SODA, STOC, and RANDOM. ERC Advanced Grant 'Mapping the Complexity of Counting' (2014-2019) Best Paper Prize at ICALP 2012, 2010, and 2006 UK Distinguished Dissertations in Computer Science (1991-1992) She actively supervises PhD students and collaborates with researchers including Andreas Galanis, Marc Roth, John Lapinskas, and Paulina Smolarova. Her work bridges theoretical computer science with statistical physics, combinatorics, and network theory, with applications to database systems, evolutionary dynamics, and communication protocols.

Phokion Kolaitisمشاهده پروفایل

استاد

• Principles of database systems
• Computational complexity
• Logic in computer science

Phokion Kolaitis is a Professor of Computer Science at the University of California Santa Cruz (UC Santa Cruz) and a Principal Research Staff Member (part-time) at the IBM Almaden Research Center. He has held leadership roles as Chair of UC Santa Cruz's Computer Science Department (1997-2001, 2014-2015) and Senior Manager at IBM's Computer Science Principles and Methodologies Department (2004-2008). His research bridges database systems, computational complexity, and logic in computer science, with significant contributions to schema mappings, data exchange, and constraint satisfaction problems. His work spans foundational studies in database theory, logic, and algorithms, influencing both academic research and industrial applications. Kolaitis has received numerous accolades, including two ACM PODS Alberto Mendelzon Test-of-Time Awards, IBM Research Division Outstanding Innovation and Technical Achievement Awards, and Best Paper Awards at top conferences like ICDT and FCT. He is a Fellow of AAAS and ACM, a Foreign Member of the Finnish Academy of Science and Letters, and holds an Honorary Doctorate from the University of Athens, Greece. 2017 Best Paper Award (FCT 2017) 2015 IBM Outstanding Innovation Award 2014 Honorary Doctorate (University of Athens) 2008 IBM Outstanding Technical Achievement Award Fellow of AAAS and ACM Foreign Member, Finnish Academy of Science Kolaitis collaborates extensively, co-authoring with researchers like D. Burdick, R. Fagin, and M.Y. Vardi. At UC Santa Cruz, he advises on database systems and logic, while his part-time role at IBM focuses on theoretical advancements in data management and complexity.

Miodrag Mihaljevicمشاهده پروفایل

استاد پژوهشی

• Cryptology
• Information Security
• Blockchain Technology
• +۱۲ مورد دیگر

Miodrag Mihaljević is a Research Professor at the Mathematical Institute , Serbian Academy of Sciences and Arts (Belgrade, Serbia) since 1999, and serves as Deputy Director of the Institute and Head of the National Center for Cyber Security and Privacy . He has held visiting positions at institutions including The University of Tokyo (1997-2023), AIST (2006-2013), and Chuo University (2013-2014), with a focus on cryptology and information security. BSEE (1979), University of Belgrade MS (1981) and Ph.D. (1990) in Electrical Engineering His research spans Cryptology , Blockchain Technology , Information Security , and Coding Theory , with significant contributions to symmetric key cryptography, stream ciphers, LPN problem analysis, broadcast encryption, and lightweight authentication protocols. His work addresses applications in IoT, cloud computing, and smart grid security, often integrating blockchain for data integrity and access control. Recent publications highlight blockchain-enabled solutions for smart contract vulnerability detection (2025), cross-chain identity authentication (2025), and secure inference in IoT (2025). Earlier works focus on cryptographic attacks (e.g., fast correlation, algebraic attacks) and security frameworks for software-defined radio and cloud systems. Scientific recognition includes the Serbian Academy of Sciences and Arts Award (2013), JSPS Fellowship (1998), and consistent inclusion in the World's Top 2% Scientists (2020-2024). He has led projects on cryptographic techniques for information security (2002-2019) and holds multiple international patents in encryption/decryption systems.

Kun Yangمشاهده پروفایل

استاد

• Internet of Things
• Wireless networks
• 6G Future internet technologies
• +۳ مورد دیگر

Kun Yang is Chair Professor at the University of Essex, UK, leading the Network Convergence Laboratory (NCL) and Communications and Networks Research Group since 2003. He has held academic roles at Essex since 2003, including Lecturer (2003-2008), Reader (2008-2011), and Chair Professor (2011-present). Fields: Internet of Things, wireless networks, data-energy integrated communication networks, and 6G technologies Labs: Founder of the Network Convergence Laboratory (2008) International Collaborations: Affiliated Professor at UESTC (China), active in EU FP7/H2020 projects His research focuses on energy self-sustainability in communication systems through energy efficiency and harvesting via resource allocation and joint optimization. Experimental work is conducted in his NCL laboratory. Recent publications span neuroscience-inspired AI, including neural sampling, cognitive maps, spiking neural networks, and secure machine learning architectures. These works intersect with Computer Science , Neuroscience , and Artificial Intelligence . 2025: Neural sampling for cognitive modeling 2024: High-dimensional prediction-learning 2023: Sparsity in forward-forward algorithms Professional Recognition: Fellow of IEEE, IET, BCS, HEA, and RSA IEEE ComSoc Distinguished Lecturer (2020-2021) Judicial role: GSMA GLOMO Awards Judge (2019-present) Research Leadership: Essex Principal Investigator in over 10 EU FP7/H2020 projects Founder of IEEE InterCloud Testbed Executive Committee

Erik Leitingerمشاهده پروفایل

عضو هیئت علمی

• Ultrawideband Wireless Communication
• Indoor Positioning
• Estimation/Detection Theory
• +۳ مورد دیگر

Erik Leitinger is a Senior Researcher at the Graz University of Technology, Austria. He holds a Dipl.-Ing. (M.Sc.) and Ph.D. in electrical engineering from the same institution, awarded in 2012 and 2016 respectively. His work focuses on ultrawideband (UWB) wireless communication, indoor positioning systems, Bayesian inference, and factor graph-based algorithms. He has contributed to multipath-based simultaneous localization and mapping (SLAM), cooperative localization, and MIMO systems. Leitinger’s research integrates theoretical frameworks like belief propagation and variational inference with practical applications in automotive radar, RFID positioning, and 5G/6G systems. His academic contributions span over 50 peer-reviewed articles, with recent work emphasizing non-ideal reflective surfaces, scalable positioning systems, and neural-enhanced SLAM algorithms. He actively collaborates with institutions like the University of Lund (Sweden) and KTH Royal Institute of Technology, contributing to projects such as the EU-funded IPIN competitions. Leitinger teaches courses on adaptive systems, signal processing, and machine learning. His student projects include topics like neural-enhanced SLAM, acoustic source localization using reflections, and real-time multipath-assisted localization with channel sounders. He is affiliated with TUGRAZonline and ORCID, maintaining an open research profile.